Publication:
Macroeconomic Shocks and Banking Sector Developments in Egypt

Loading...
Thumbnail Image
Files in English
English PDF (984.39 KB)
527 downloads
English Text (130.34 KB)
104 downloads
Date
2013-01
ISSN
Published
2013-01
Author(s)
Youssef, Hoda
Editor(s)
Abstract
From 2008 to 2011, Egypt was hit by significant shocks, both global and country-specific. This paper assesses the impact of the resulting macroeconomic instability on the banking sector, and examines its role as a shock absorber. The Central Bank of Egypt accommodated the shocks by supplying liquidity to the market. The paper verifies a change in the fiscal regime from one in which the primary fiscal balance was used an instrument to stabilize the public debt ratio to one in which the policy instrument stopped playing that role and affected investors' assessment of the risk of holding public debt. This pattern suggests that fiscal conditions influenced exchange rate and price expectations originating a fiscal dominance situation in which the Central Bank could not control inflation. Hence, the Central Bank lacked functional independence in spite of its de jure independence, which underscores the importance of strengthening institutions that facilitate policy coordination and allow policy to be more predictable. The government also funds itself through non-market mechanisms, in a typical financial repression scheme. The paper estimates the revenue from financial repression at about 2.5 percent of gross domestic product in 2011, which together with the revenues from seignoriage add up to close to 50 percent of the budgeted tax revenues, indicating the need for an in-depth review of the governance of the public banks and the funding of public sector activities. Finally, the paper estimates the impact of shocks to macroeconomic variables on loan portfolio quality and bank capital.
Link to Data Set
Citation
Youssef, Hoda; Herrera, Santiago. 2013. Macroeconomic Shocks and Banking Sector Developments in Egypt. Policy Research Working Paper; No. 6314. © World Bank, Washington, DC. http://hdl.handle.net/10986/12179 License: CC BY 3.0 IGO.
Associated URLs
Associated content
Report Series
Report Series
Other publications in this report series
  • Publication
    The Exposure of Workers to Artificial Intelligence in Low- and Middle-Income Countries
    (Washington, DC: World Bank, 2025-02-05) Demombynes, Gabriel; Langbein, Jörg; Weber, Michael
    Research on the labor market implications of artificial intelligence has focused principally on high-income countries. This paper analyzes this issue using microdata from a large set of low- and middle-income countries, applying a measure of potential artificial intelligence occupational exposure to a harmonized set of labor force surveys for 25 countries, covering a population of 3.5 billion people. The approach advances work by using harmonized microdata at the level of individual workers, which allows for a multivariate analysis of factors associated with exposure. Additionally, unlike earlier papers, the paper uses highly detailed (4 digit) occupation codes, which provide a more reliable mapping of artificial intelligence exposure to occupation. Results within countries, show that artificial intelligence exposure is higher for women, urban workers, and those with higher education. Exposure decreases by country income level, with high exposure for just 12 percent of workers in low-income countries and 15 percent of workers in lower-middle-income countries. Furthermore, lack of access to electricity limits effective exposure in low-income countries. These results suggest that for developing countries, and in particular low-income countries, the labor market impacts of artificial intelligence will be more limited than in high-income countries. While greater exposure to artificial intelligence indicates larger potential for future changes in certain occupations, it does not equate to job loss, as it could result in augmentation of worker productivity, automation of some tasks, or both.
  • Publication
    Indigenous peoples, land and conflict in Mindanao, Philippines
    (Washington, DC: World Bank, 2024-02-12) Madrigal Correa, Alma Lucia; Cuesta Leiva, Jose Antonio; Somerville, Sergio Patrick
    This article explores the links between conflict, land and indigenous peoples in several regions of Mindano, the Philippines, notorious for their levels of poverty and conflict. The analysis takes advantage of the unprecedented concurrence of data from the most recent, 2020, census; an independent conflict data monitor for Mindanao; and administrative sources on ancestral land titling for indigenous peoples in the Philippines. While evidence elsewhere compellingly links land titling with conflict reduction, a more nuanced story emerges in the Philippines. Conflicts, including land- and resource-related conflicts, are generally less likely in districts (barangays) with higher shares of indigenous peoples. Ancestral domain areas also have a lower likelihood for general conflict but a higher likelihood for land-related conflict. Ancestral domains titling does not automatically solve land-related conflicts. When administrative delays take place (from cumbersome bureaucratic processes, insufficient resources and weak institutional capacity), titling processes may lead to sustained, rather than decreased, conflict.
  • Publication
    Who on Earth Is Using Generative AI ?
    (Washington, DC: World Bank, 2024-08-22) Liu, Yan; Wang, He
    Leveraging unconventional data, including website traffic data and Google Trends, this paper unveils the real-time usage patterns of generative artificial intelligence tools by individuals across countries. The paper also examines country-level factors driving the uptake and early impacts of generative artificial intelligence on online activities. As of March 2024, the top 40 generative artificial intelligence tools attract nearly 3 billion visits per month from hundreds of millions of users. ChatGPT alone commanded 82.5 percent of the traffic, yet reaching only one-eightieth of Google’s monthly visits. Generative artificial intelligence users skew young, highly educated, and male, particularly for video generation tools, with usage patterns strongly indicating productivity-related activities. Generative artificial intelligence has achieved unprecedentedly rapid global diffusion, reaching almost all economies worldwide within 16 months of ChatGPT’s release. Middle-income economies have disproportionately high adoption of generative artificial intelligence relative to their economic scale, now contribute more than 50 percent of global traffic, while low-income economies contribute less than 1 percent. Regression analysis reveals that income level, share of youth population, digital infrastructure, specialization in high-skill tradable services, English proficiency, and human capital are strongly correlated with higher uptake of generative artificial intelligence. The paper also documents disruptions in online traffic patterns and emphasizes the need for targeted investments in digital infrastructure and skills development to harness the full potential of artificial intelligence.
  • Publication
    Design of Partial Population Experiments with an Application to Spillovers in Tax Compliance
    (Washington, DC: World Bank, 2025-02-07) Cruces, Guillermo; Tortarolo, Dario; Vazquez-Bare, Gonzalo
    This paper develops a framework to analyze partial population experiments, a generalization of the cluster experimental design where clusters are assigned to different treatment intensities. The framework allows for heterogeneity in cluster sizes and outcome distributions. The paper studies the large-sample behavior of OLS estimators and cluster-robust variance estimators and shows that (i) ignoring cluster heterogeneity may result in severely underpowered experiments and (ii) the cluster-robust variance estimator may be upward-biased when clusters are heterogeneous. The paper derives formulas for power, minimum detectable effects, and optimal cluster assignment probabilities. All the results apply to cluster experiments, a particular case of the framework. The paper sets up a potential outcomes framework to interpret the OLS estimands as causal effects. It implements the methods in a large-scale experiment to estimate the direct and spillover effects of a communication campaign on property tax compliance. The analysis reveals an increase in tax compliance among individuals directly targeted with the mailing, as well as compliance spillovers on untreated individuals in clusters with a high proportion of treated taxpayers.
  • Publication
    Dynamic, High-Resolution Wealth Measurement in Data-Scarce Environments
    (Washington, DC: World Bank, 2025-02-06) Zheng, Zhuo; Wu, Timothy; Lee, Richard; Newhouse, David; Kilic, Talip; Burke, Marshall; Ermon, Stefano; Lobell, David B.
    Accurate and comprehensive measurement of household livelihoods is critical for monitoring progress toward poverty alleviation and targeting social assistance programs for those who most need it. However, the high cost of traditional data collection has historically made comprehensive measurement a difficult task. This paper evaluates alternative satellite-based deep learning approaches using detailed household census extracts from four African countries to accelerate progress toward comprehensive, fine-scale, and dynamic measurement of asset wealth at scale. The results indicate that transformer architectures solve multiple open measurement problems, by providing the most accurate measurement of local-level variation in household asset wealth across countries and cities, as well as changes in household asset wealth over time. Experiments that artificially restrict data availability show the model’s ability to achieve high performance with limited data. The proposed approach demonstrates the promise of combining satellite imagery, publicly available geo-features, and new deep learning architectures for hyperlocal and dynamic measurement of wealth in data-scarce environments.
Journal
Journal Volume
Journal Issue
Citations